Classical knowledge representation methods traditionally work with established relations such as synonymy, hierarchy and unspecified associations. Recent developments like ontologies and folksonomies show new forms of collaboration, indexing and knowledge representation and encourage the reconsideration of standard knowledge relationships. In a summarizing overview we show which relations are c...

. This paper describes KRL, a Knowledge Representation Language designed for use in’ understander systems. It outlines both the general.concepts which underlie our research and the details of K RL-0, an experimental implementation of some of these concepts. KRL is an attempt to integrate procedural knowledge with a broad base of declarative forms. These forms provide a variety of ways to expres...

! ‚Concept‘ as atomic unit of knowledge represents a class of entities of a discourse domain ! Concept is: " named after the elements of the class " graphically illustrated as ellipse, that is framing its name ! A concept, that represents a set S, is denoted as |C|S ! If S ⊂ T, then: |C|S subconcept (Sub C) of |C|T |C|T super concept (SuperC) of |C|S ! This relation is: " called concept subsump...

In th is paper I introduce a contrast between homomorphic and non-homomorphic ascript ions of informational content to representations. In the former case there is a mapping from the parts of the representation onto the constituents of the content. In the l a t t e r case, there is not; some of the constituents of the content are set t led bj background factors. I contrast th is d i s t i nc t ...

Knowledge representation has long had the insight that one data model, if it is expressive enough, is both necessary and sufficient for adequately storing information. Software engineering, alas, has gone a different way and uses an assortment of models. This model heterogeneity leads to problems of understanding and maintenance, because mappings between them are unavoidable and complicated. We...

One of the main areas in knowledge representation and logic-based artificial intelligence concerns logical formalisms that can be used for representing and reasoning with concepts. For almost 30 years, since research in this area began, the issue of intensionality has had a special status in that it has been considered to play an important role, yet it has not been precisely established what it...

Semantic metadata describing subject content plays a vital role in supporting indexing and retrieval in Digital Libraries. Mechanisms used to deliver this metadata include keyword collections, thesauri and classi®cations. Constructing a large thesaurus, however, is a dicult process which can be facilitated through the application of knowledge representation techniques developed for managing an...

In the artificial intelligence field, knowledge representation and reasoning are important areas for intelligent systems, especially knowledge base systems and expert systems. Knowledge representation Methods has an important role in designing the systems. There have been many models for knowledge such as semantic networks, conceptual graphs, and neural networks. These models are useful tools t...

Formalization presupposes ‘precisification’. A formal representation, therefore, cannot account for all relevant aspects of imprecise domain knowledge. In this paper we present a methodology for dealing with this problem. In an imprecise domain, part of the expertise is to know the realm within which knowledge may be faithfully specialized. In a computer reasoning system, such expert knowledge ...

Consider an intelligence analyst who has a large body of documents of various kinds. He would like answers to some of his questions based on the information in these documents, general knowledge available in compilations such as fact books, and commonsense. A search engine or a typical information retrieval (IR) system like Google does not go far enough as it takes keywords and only gives a ran...